Autoencoders (AE)

The aim of an autoencoder is to learn a representation for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”.


Introduction to Autoencoders
A look at autoencoders for representation learning.
autoencoders representation-learning tutorial article
From Autoencoder to Beta-VAE
This post reviews several variations, including denoising, sparse, and contractive autoencoders, and VAE and its modification beta-VAE.
autoencoders variational-autoencoders beta-vae tutorial


Intro to Autoencoders
This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.
autoencoders anomaly-detection time-series denoising
A Tutorial on VAEs: From Bayes' Rule to Lossless Compression
An overview of the VAE and a tour through various derivations and interpretations of the VAE objective.
variational-autoencoders autoencoders bayes-rule loseless-compression
How to Detect Data-Copying in Generative Models
I propose some new definitions and test statistics for conceptualizing and measuring overfitting by generative models.
generative-modeling data-copying generative-adversarial-networks variational-autoencoders


Unsupervised Toolbox
Unsupervised learning Tool box : A micro framework for State of the Art Methods and models for unsupervised learning for NLU / NLG
question-generation question-answering question-similarity autoencoders
PYRO: Deep Universal Probabilistic Programming
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch.
pyro probabilistic-programming library pytorch
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